Convolutional Neural Networks in Visual Computing

A Concise Guide

Description

Convolutional Neural Networks in Visual Computing: A Concise Guide covers the fundamentals of
designing and deploying deep convolutional neural network architectures. It is intended to serve
as a beginner’s guide for engineers and students who want to have a quick start on learning and/or
building deep vision systems. This book provides a good theoretical and practical understanding
along with a complete toolkit for basic information and knowledge required to understand and
build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional
neural networks, filtering out other material that co-occur in many advanced books on CNN topics.

This book is:

Comprehensive and fundamental enough to cover what is needed to develop and implement CNNs

Self-contained for audiences outside the computer science research domain, e.g., audiences in industry

Easy to understand and well-illustrated with small examples and case studies along with code-snippets and data sets

Helpful for early graduate students and college seniors who want to explore the field with some hands-on experience

A single source to self-learn concepts, methods and software tools required to completely and single-handedly implement state-of-the-art CNN systems

This website is a supplementary to the book and contains code and implementations, color illustrations of some figures and additional discusions. This book
also led to a graduate level course that was taught in the Spring of 2017 at Arizona State University, lectures and materials for which is also avialable
here.